This course covers Bayesian statistics, focusing on updating inferences about parameters or hypotheses as evidence accumulates. You'll learn to use Bayes' rule to transform prior probabilities into posterior probabilities and explore the underlying theory of the Bayesian paradigm. The course includes practical applications of Bayesian methods, demonstrating complete Bayesian analyses from framing questions to implementing posterior distributions in R.
🆓 Free to Audit
🕒 Approx. 34 Hours
✏️ Intermediate Level
🧾 Paid Certificate Available Upon Completion
🎓 Offered by Duke University via Coursera
